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Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee

Won Ik Cho, Soomin Kim, Eujeong Choi, Younghoon Jeong


Abstract
There is an ongoing discussion on what makes humans more engaged when interacting with conversational agents. However, in the area of language processing, there has been a paucity of studies on how people react to agents and share interactions with others. We attack this issue by investigating the user dialogues with human-like agents posted online and aim to analyze the dialogue patterns. We construct a taxonomy to discern the users’ self-disclosure in the dialogue and the communication authenticity displayed in the user posting. We annotate the in-the-wild data, examine the reliability of the proposed scheme, and discuss how the categorization can be utilized for future research and industrial development.
Anthology ID:
2022.findings-aacl.14
Volume:
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
145–152
Language:
URL:
https://aclanthology.org/2022.findings-aacl.14
DOI:
10.18653/v1/2022.findings-aacl.14
Bibkey:
Cite (ACL):
Won Ik Cho, Soomin Kim, Eujeong Choi, and Younghoon Jeong. 2022. Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 145–152, Online only. Association for Computational Linguistics.
Cite (Informal):
Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee (Cho et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-aacl.14.pdf